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Validation of a Computerized Technique for Automatically Tracking and Measuring the Inferior Vena Cava in Ultrasound Imagery

机译:超声图像中自动跟踪和测量下腔静脉的计算机化技术验证

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Accurate resuscitation of the critically-ill patient using intravenous fluids and blood products is a challenging, time sensitive task. Ultrasound of the inferior vena cava (IVC) is a non-invasive technique currently used to guide fluid administration, though multiple factors such as variable image quality, time, and operator skill challenge mainstream acceptance. This study represents a first attempt to develop and validate an algorithm capable of automatically tracking and measuring the IVC compared to human operators across a diverse range of image quality. Minimal tracking failures and high levels of agreement between manual and algorithm measurements were demonstrated on good quality videos. Addressing problems such as gaps in the vessel wall and intra-lumen speckle should result in improved performance in average and poor quality videos. Semi-automated measurement of the IVC for the purposes of non-invasive estimation of circulating blood volume poses challenges however is feasible.
机译:使用静脉内流体和血液制品准确复苏,血液制品是一个具有挑战性的,时光敏感的任务。下腔静脉(IVC)的超声是目前用于引导流体给药的非侵入性技术,但多个因素如可变图像质量,时间和操作员技能挑战主流验收。该研究代表了第一次尝试开发和验证能够自动跟踪和测量IVC的算法,与人类运营商相比各种图像质量相比。在良好的质量视频上证明了手动和算法测量之间的最小跟踪故障和高度协议。解决船舶墙壁和腔内斑点差距等问题,应导致平均和质量差的态度提高性能。然而,对于循环血容量的非侵入性估计的目的,IVC的半自动测量造成挑战然而是可行的。

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